Resize meso benchmarks with iteration time < 1s

The resized benchmarks have suffix "-sized2". The original benchmarks
will be kept around for a little while to collect results from both
versions in parallel.

Some benchmarks had to be updated to support parametrization. Other
benchmarks were just copied to a new file. The old file will be deleted
later with the old benchmark, so there will be no duplication
eventually.

Author: steve-s

graalpython/com.oracle.graal.python.benchmarks/python/host_interop/image-magix-sized2.py

@@ -0,0 +1,142 @@
+# Copyright (c) 2017, 2021, Oracle and/or its affiliates.
+# Copyright (c) 2013, Pablo Mouzo
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+from array import array
+from math import sqrt, atan2, sqrt, sin, cos, ceil, floor
+
+
+class Image(object):
+    def __init__(self, width, height, data=None):
+        self.width = width
+        self.height = height
+        if data:
+            self.data = data
+        else:
+            self.data = [0] * (width * height)
+
+    def _idx(self, x, y):
+        if 0 <= x < self.width and 0 <= y < self.height:
+            return y * self.width + x
+        raise IndexError
+
+    def __getitem__(self, t):
+        x, y = t
+        return self.data[self._idx(x, y)]
+
+    def __setitem__(self, t, val):
+        x, y = t
+        self.data[self._idx(x, y)] = val
+
+    def pixels(self, border=0):
+        for y in xrange(border, self.height - border):
+            for x in xrange(border, self.width - border):
+                yield x, y
+
+    def sobel(self, horizontal=True, vertical=True):
+        out = Image(self.width, self.height)
+        for y in range(1, self.height - 1):
+            for x in range(1, self.width - 1):
+                if horizontal:
+                    dx = -1.0 * self[x - 1, y - 1] + 1.0 * self[x + 1, y - 1] + \
+                         -2.0 * self[x - 1, y]     + 2.0 * self[x + 1, y] + \
+                         -1.0 * self[x - 1, y + 1] + 1.0 * self[x + 1, y + 1]
+                else:
+                    dx = self[x, y]
+                if vertical:
+                    dy = -1.0 * self[x - 1, y - 1] - 2.0 * self[x, y - 1] - 1.0 * self[x + 1, y - 1] + \
+                         1.0 * self[x - 1, y + 1] + 2.0 * self[x, y + 1] + 1.0 * self[x + 1, y + 1]
+                else:
+                    dy = self[x, y]
+                out[x, y] = min(int(sqrt(dx * dx + dy * dy) / 4.0), 255)
+        return out
+
+    def fisheye(img, fraction=2, bilinear=False):
+        if bilinear:
+            img = BilinImage(img.width, img.height, data=img.data)
+        else:
+            img = NNImage(img.width, img.height, data=img.data)
+        out = Image(img.width, img.height, data=img.data[:])
+        maxr = img.height / (fraction + 1)
+        for y in range(int(img.height / 2 - maxr), int(img.height / 2 + maxr)):
+            for x in range(int(img.width / 2 - maxr), int(img.width / 2 + maxr)):
+                dx, dy = x - img.width / 2, y - img.height / 2
+                a = atan2(dy, dx)
+                r = sqrt(dx ** 2 + dy ** 2)
+                if r < maxr:
+                    nr = r * r / maxr
+                    nx, ny = nr * cos(a), nr * sin(a)
+                    out[x,y] = min(int(img[nx + img.width / 2, ny + img.height / 2]), 255)
+                else:
+                    out[x,y] = img[x,y]
+        return out
+
+
+class NNImage(Image):
+    def __getitem__(self, t):
+        x, y = t
+        return Image.__getitem__(self, (int(x + 0.5), int(y + 0.5)))
+
+
+class BilinImage(Image):
+    def __getitem__(self, t):
+        x, y = t
+        if isinstance(x, float) and isinstance(y, float):
+            x0, x1 = int(floor(x)), int(ceil(x))
+            y0, y1 = int(floor(y)), int(ceil(y))
+            xoff, yoff = x - x0, y - y0
+            return (1.0 - xoff) * (1.0 - yoff) * self[x0, y0] + \
+                   (1.0 - xoff) * (      yoff) * self[x0, y1] + \
+                   (      xoff) * (1.0 - yoff) * self[x1, y0] + \
+                   (      xoff) * (      yoff) * self[x1, y1]
+        else:
+            return Image.__getitem__(self, (x, y))
+
+
+SZ = 20
+import java
+INPUT_DATA = java.type("int[]")(SZ * SZ)
+for i in range(SZ * SZ):
+    INPUT_DATA[i] = i
+
+
+def measure(num):
+    img = Image(SZ, SZ, data=INPUT_DATA)
+    for i in range(num):
+        img = img.sobel(horizontal=True, vertical=True)
+        img = img.fisheye(bilinear=True, fraction=3)
+    return img
+
+
+def __benchmark__(num=10000):
+    return measure(num)
+
+
+if __name__ == '__main__':
+    import sys
+    import time
+    start = time.time()
+    if len(sys.argv) >= 2:
+        num = int(sys.argv[1])
+        img = __benchmark__(num)
+    else:
+        img = __benchmark__(2)
+    print(img.data)
+    print("%s took %s s" % (__file__, time.time() - start))

graalpython/com.oracle.graal.python.benchmarks/python/meso/backprop_rodinia-sized2.py

@@ -0,0 +1,213 @@
+# Copyright (c) 2020, 2021, Oracle and/or its affiliates. All rights reserved.
+# DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
+#
+# The Universal Permissive License (UPL), Version 1.0
+#
+# Subject to the condition set forth below, permission is hereby granted to any
+# person obtaining a copy of this software, associated documentation and/or
+# data (collectively the "Software"), free of charge and under any and all
+# copyright rights in the Software, and any and all patent rights owned or
+# freely licensable by each licensor hereunder covering either (i) the
+# unmodified Software as contributed to or provided by such licensor, or (ii)
+# the Larger Works (as defined below), to deal in both
+#
+# (a) the Software, and
+#
+# (b) any piece of software and/or hardware listed in the lrgrwrks.txt file if
+# one is included with the Software each a "Larger Work" to which the Software
+# is contributed by such licensors),
+#
+# without restriction, including without limitation the rights to copy, create
+# derivative works of, display, perform, and distribute the Software and make,
+# use, sell, offer for sale, import, export, have made, and have sold the
+# Software and the Larger Work(s), and to sublicense the foregoing rights on
+# either these or other terms.
+#
+# This license is subject to the following condition:
+#
+# The above copyright notice and either this complete permission notice or at a
+# minimum a reference to the UPL must be included in all copies or substantial
+# portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+"""
+ ******************************************************************
+ * HISTORY
+ * 15-Oct-94  Jeff Shufelt (js), Carnegie Mellon University
+ *	Prepared for 15-681, Fall 1994.
+ * Modified by Shuai Che
+ ******************************************************************
+"""
+
+import random
+import math
+
+# eta value
+ETA = 0.3
+
+# momentum value
+MOMENTUM = 0.3
+
+# (16, 1 can not be changed)
+n_hidden = 16
+n_out = 1
+
+
+def bpnn_layerforward(l1, l2, conn, n1, n2):
+    ## Set up thresholding unit ##
+    l1[0] = 1.0
+    ## For each unit in second layer ##
+    r1 = 1 + n2
+    r2 = 1 + n1
+    for j in range(1, r1):
+        ## Compute weighted Sum of its inputs ##
+        Sum = 0.0
+        for k in range(r2):
+            Sum += conn[k*r1 + j] * l1[k]
+
+        l2[j] = (1.0 / (1.0 + math.exp(-Sum)))
+
+
+def bpnn_output_error(delta, target, output, nj):
+    errSum = 0.0
+    for j in range(1, 1 + nj):
+        o = output[j]
+        t = target[j]
+        v = o * (1.0 - o) * (t - o)
+        delta[j] = v
+        errSum += abs(v)
+
+    return errSum
+
+
+def bpnn_hidden_error(delta_h, nh, delta_o, no, who, hidden):
+    errSum = 0.0
+    for j in range(1, 1 + nh):
+        Sum = 0.0
+        for k in range(1, 1 + no):
+            Sum += delta_o[k] * who[j * (1 + no) + k]
+        h = hidden[j]
+        delta_h[j] = h * (1.0 - h) * Sum
+        errSum += abs(delta_h[j])
+
+    return errSum
+
+
+def bpnn_adjust_weights(delta, ndelta, ly, nly, w, oldw):
+    ly[0] = 1.0
+    for j in range(1, (1 + ndelta)):
+        for k in range(nly + 1):
+            val = ETA * delta[j] * ly[k]
+            val += MOMENTUM * oldw[k*(1 + ndelta) + j]
+            oldw[k*(1 + ndelta) + j] = val
+            w[k*(1 + ndelta) + j] += val
+
+
+def bpnn_train_kernel(_iu_list, _hu_list, _iw_list, _ou_list, _hw_list, _od_list, _t_list, _hd_list, _hw_prev_list, _iw_prev_list, layer_size):
+    bpnn_layerforward(_iu_list, _hu_list, _iw_list, layer_size, n_hidden)
+    bpnn_layerforward(_hu_list, _ou_list, _hw_list, n_hidden, n_out)
+    out_err = bpnn_output_error(_od_list, _t_list, _ou_list, n_out)
+    hid_err = bpnn_hidden_error(
+        _hd_list, n_hidden, _od_list, n_out, _hw_list, _hu_list)
+    bpnn_adjust_weights(_od_list, n_out, _hu_list,
+                        n_hidden, _hw_list, _hw_prev_list)
+    bpnn_adjust_weights(_hd_list, n_hidden, _iu_list,
+                        layer_size, _iw_list, _iw_prev_list)
+
+    return (out_err, hid_err)
+
+
+class Data:
+    def __init__(self):
+        self._iu_list = None
+        self._hw_list = None
+        self._hu_list = None
+        self._ou_list = None
+        self._hd_list = None
+        self._od_list = None
+        self._iw_list = None
+        self._iw_prev_list = None
+        self._hw_prev_list = None
+        self._t_list = None
+
+        self.zeros_list = None
+        self.random_list = None
+
+
+data = Data()
+
+default_size = 2 ** 16
+
+
+def measure(layer_size=default_size):
+    print("Starting training kernel")
+    bpnn_train_kernel(data._iu_list, data._hu_list, data._iw_list, data._ou_list, data._hw_list,
+                      data._od_list, data._t_list, data._hd_list, data._hw_prev_list, data._iw_prev_list, layer_size)
+
+
+def __benchmark__(layer_size=default_size):
+    measure(layer_size)
+
+
+def __setup__(layer_size=default_size):
+    random.seed(7)
+    print("Input layer size : %d" % layer_size)
+    # Creates a new fully-connected network from scratch,
+    # with the given numbers of input, hidden, and output units.
+    # Threshold units are automatically included.  All weights are
+    # randomly initialized.
+
+    # Space is also allocated for temporary storage (momentum weights,
+    # error computations, etc).
+
+    ## the input units ##
+    data._iu_list = [random.random() for i in range(layer_size + 1)]
+
+    ## weights from hidden to output layer ##
+    data._hw_list = [random.random()
+                     for i in range((n_out + 1) * (n_hidden + 1))]
+
+    ## the hidden units ##
+    data._hu_list = [0. for i in range(n_hidden + 1)]
+    ## the output units ##
+    data._ou_list = [0. for i in range(n_out + 1)]
+
+    ## storage for hidden unit error ##
+    data._hd_list = [0. for i in range(n_hidden + 1)]
+    ## storage for output unit error ##
+    data._od_list = [0. for i in range(n_out + 1)]
+
+    ## weights from input to hidden layer ##
+    data._iw_list = [0. for i in range((n_hidden + 1) * (layer_size + 1))]
+
+    ## The next two are for momentum ##
+    ## previous change on input to hidden wgt ##
+    data._iw_prev_list = [0. for i in range((n_hidden + 1) * (layer_size + 1))]
+    ## previous change on hidden to output wgt ##
+    data._hw_prev_list = [0. for i in range((n_out + 1) * (n_hidden + 1))]
+
+    ## storage for target vector ##
+    data._t_list = [0.1 for i in range(n_out + 1)]
+
+    data.zeros_list = [data._hu_list, data._ou_list, data._hd_list,
+                       data._od_list, data._iw_list, data._iw_prev_list, data._hw_prev_list]
+    data.random_list = [data._iu_list, data._hw_list]
+
+
+def __cleanup__(layer_size=default_size):
+    # clean up written data
+    for l in data.zeros_list:
+        for i in range(len(l)):
+            l[i] = 0.
+    for l in data.random_list:
+        for i in range(len(l)):
+            l[i] = random.random()
+    for i in range(len(data._t_list)):
+        data._t_list[i] = 0.1